Vibration analysis, applied on a scheduled basis for machine-conditioning monitoring, has proven itself to be very beneficial toward controlling cost associated with maintenance of industrial machinery. The general procedure for implementation is to acquire data and data analysis on a predefined schedule. The equipment employed typically is portable data collector/analyzer combined with a software package residing on a personal computer or Internet.
In addition to the oscillatory motion (vibration) introduced on a macroscopic scale, many faults commonly experienced in bearings, gears, etc., also introduce, on a microscopic scale, stress wave packets which propagate away from the initiation site at the speed of sound in the media (e.g., metal). These stress wave packets are short-term, fractional to a few milliseconds, transient events which accompany events such as metal-to-metal impacting, fatigue cracking, friction, and similar events. The stress waves induce an output in an accelerometer (which responds to absolute motion) or other sensor. Since an accelerometer is generally the sensor of choice for the macroscopic (vibration) motion monitoring, it is logical to also adopt the accelerometer as the sensor of choice for stress wave analysis. Detailed analysis of these stress wave packets provides valuable insight to the presence of mechanical faults as well as assistance in identifying the severity of the fault. Typically, the stress activity is easily separated from the more macroscopic (vibration) motion activity simply by routing the sensor (accelerometer) through a high pass filter (or a band pass filter) to reject the lower frequency vibration driven component of the sensor (accelerometer) output.
Several papers and patents have been published over the years on the analysis of stress waves. One of the most commonly used methods is a peak value method described in U.S. Pat. No. 5,895,857 to Robinson et al. In general, the peak value method comprises the steps of: a.) sensing motion, including a stress wave component; b.) separating the stress wave component from other components with a high pass filter to create a signal proportional to the stress wave; c.) processing the amplified signal with a sample and hold peak detector over a predetermined interval of time to determine peaks of the amplified signal over said predetermined period of time; e.) creating an output signal proportional to the determined peaks of the amplified signal and performing the Fast Fourier Transform to detect any frequency peaks corresponding to the frequency of events causing the stress waves.